• Economic Analysis
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• v.26 no.3
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• pp.71-133
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• 2020

The Bank of Korea began its operation on June 12, 1950, with the Bank of Korea Act established a month or so earlier. Thus was first introduced to Korea modern central banking in the real sense of the word. The Bloomfield Mission, consisting of A. Bloomfield and J. Jensen of the Federal Reserve Bank of New York, spent about six months drafting a bill, which finally became the Bank of Korea Act. Little has been known yet about the process leading to the creation of the Mission and the historical context surrounding it, except that F. Tamagna of the Federal Reserve Board made in his capacity of the ECA's representative the offer of technical assistance to the Korean government. This paper attempts to dig deeper into relevant historical records and literature to fill these gaps. As it happened, the confrontation between the US and the USSR was accelerating towards the end of 1940s. The paper's new findings include that the Bloomfield Mission was, together with the ECA Mission to Korea, a product of the then US foreign policy (Cold War policy) and that the former Mission's technical assistance was conceived and provided all along as part of the inflation stabilization program pursued by the latter Mission. The Bloomfield Mission was after all a historical necessity. Next, the paper examines the changes added to the bill during its journey to becoming the Bank of Korea Act enacted in May 1950, presenting a review of the Act. The paper further evaluates the Act in terms of legal persistence, finding that the revised Act currently in force still substantially resembles the Act enacted 70 years ago from now. Finally in order is a brief discussion on those factors which seem to have contributed much to such persistence and thus apparent excellence of the Act enacted.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.4
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• pp.169-179
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• 2016

This study was to investigate special education teachers' teaching in reading and writing to university students with intellectual disabilities. For this study, we surveyed 71special education teachers who work in Gwangju, Daegu and Busan. As a result, in order to identify students' reading and writing abilities, they were more likely to use their own tests which they made for themselves than standardized tests. When teaching students, they used their own teaching experiences and advices from colleagues and senior teachers regarding problem-solving methods and reliable information while the knowledge they learned at school showed low frequency in use. Despite using mainly whole-word approach when instructing reading and writing, it appeared that teachers' teaching experiences and diversity of textbooks also have an influence. Regarding needs of education participation for teaching students, there were high needs and interests in teaching methods of writing, textbooks and teaching materials by the characteristics of disability, reading and writing development, reading and writing disabilities. In case of difficulties and needs in teaching students, there was a high demand of development of a wide variety of teaching materials and tools and, preparation for sufficient textbooks and test tools, while difficulties in teaching appeared in lack of knowledge about reading and writing, lack of screening/evaluating tools, and evaluating and teaching oriented to each disability characteristic.

• The Korean Journal of Franchise Management
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• v.9 no.3
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• pp.45-55
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• 2018

Purpose - Non-verbal Communication with customers in restaurant business can play an important role because it affects customer behavior and attitudes as a means to develop and maintain long-term relationships with customers. The purpose of this study is to analyze the effect of non-verbal communication with customers and the effect of the influence on customer satisfaction, trust, and revisit intention. Research design, data, methodology - In order to verify the research models and hypotheses of this study, questions were prepared for each variable and data were collected through questionnaires. The questionnaire survey was conducted from March 27, 2018 to April 17, 2018, for those who agreed with the citizens of the Jeju area who visited the restaurant recently. 50 out of 100 were conducted by internet survey and 50 were surveyed. Thus, a total of 100 responses were used using structural equation modeling with Smartpls 3.0. Results - The results of the study are as follows. First, non-verbal communication has a significant impact on customer emotion. Second customer emotion have a significant impact on customer trust and satisfaction. Third, Customer satisfaction had positive a significant effect on revisit intention. Fourth, Customer trust had positive a significant effect on revisit intention. Conclusions - The implications of this study are following as: The food service company should continuously provide non-verbal communication training to employees so that they can respond to customers with the right attitude and bright smile. In particular, in the case of restaurant franchises, customer response manuals should be created and distributed to the franchisees, and a regular training program for the franchisees should be implemented to provide the same service to the customer. Second, CEOs should have to worry about what kind of experience he or she has left since leaving the store. It is also necessary to constantly look at what customers experience in their stores or in their brands, and what emotions they form through their experiences. Third, the more satisfied or trusted customers are formed through the service of the employee, the more loyal the restaurant business will be, and the more likely it is to make continuous revisit and positive word-of-mouth activities..

• Journal of the Korean Applied Science and Technology
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• v.40 no.5
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• pp.1072-1080
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• 2023

The purpose of this study is to analyze major keywords and social phenomena related to 'plogging' in the sports field and to derive important information. In order to achieve this purpose, the news analysis system BIG Kinds provided by the Korea Press Promotion Foundation was used to analyze it. The analysis period is from 2018 to 2022, and 42 of the 5,148 news collected were finally used and analyzed. Frequency analysis, relationship map analysis, and related word analysis were performed as analysis methods, and the results are as follows. First, as a result of the frequency analysis related to 'Plogging' in the sports field, keywords such as 'Jeju', 'Players', 'World Marathon', 'SSG', and 'Lee Bong-ju' were identified. Second, as a result of the analysis of the relationship related to 'Plogging' in the sports field, keywords such as 'COVID-19', 'national representative', 'elite', 'masters', and 'COVID' were identified. Third, as a result of the analysis of words related to 'Plogging' in the sports field, keywords such as 'synthesized words', 'volunteer activities,' 'masters untact', 'Jeju', and 'athletes' were identified. In the domestic professional sports field, it has been shown that plogging is actively used for environmental activities and professional team promotion to practice carbon neutrality by international sports organizations.

• Knowledge Management Research
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• v.24 no.4
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• pp.219-235
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• 2023

The advancement of digital technology and the impact of COVID-19 have brought about changes in corporate innovation and organizational culture, thereby highlighting the significance of Smart Learning in the field of HRD (Human Resource Development). This trend has led to an increased interest in personalized Smart Learning among employees due to the growth of hybrid work and the widespread adoption of smart work practices. This study aimed to illuminate the relative importance of the factors that constitute Smart Learning from the perspective of HRD practitioners. Through a review of prior literature, Smart Learning hierarchy and factors most fitting to the current context were identified, and their relative importance was determined using the AHP method. Consequently, in the first-tier factors, importance was confirmed in the order of 'Learning Activities', 'Teaching Activities', 'Learning Content', 'Assessment and Evaluations', and 'Learning Time and Space'. At the second-tier encompassing all factors, 'Pedagogical Strategy', 'Learning Results', 'Learning Tasks', 'Learning Goal', and 'Learning Support' emerged within the top five factors. These findings are significant in that they redefine the concept of smart learning and propose an academic framework for future research. Additionally, from a practical perspective, it is anticipated that this study will contribute valuable insights for HRD practitioners, aiding them in focusing on which factors to prioritize for enhancing and advancing Smart Learning initiatives.

• Information Systems Review
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• v.23 no.1
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• pp.45-68
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• 2021

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

• Information Systems Review
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• v.23 no.3
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• pp.25-50
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• 2021

Though there have been a lot of studies about online product review, the effects of highlighted reviewhave not been examined enough. Highlighted review is a type of review that the platform designer changes its size or position in order to highlight without any sponsorship or incentive. The main subject of this study is about how highlighted review type affects consumer's perception and behavior in online information acquisition. We collected data from 171 subjects to test hypotheses. Using three different types of screen captures, we compared three groups - general review group, positive highlighted review only group, and both positive and negative highlighted review group. As a result, disclosing both of positiveand negative highlighted review was perceived more useful than disclosing only positive highlighted review. However, correlation between highlighted review type and review skepticism was not statistically significant. The impacts of review usefulness and skepticism on platform credibility were statistically significant, and the correlation between platform credibility and usage intention was also significant. All of results is almost similar across two product types, search goods and experiential goods. This research provides practical implications to online shopping platform designers when they design review systems to make people use their platforms.

• Journal of Sericultural and Entomological Science
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• v.23 no.1
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• pp.47-55
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• 1981

Silk textile finishing has been studied for many years by many workers in order to meet more utilities for various endusers. Such studies, however, could not be successful because any natural fibers are hardly change their natures by artificial treating methods. Textile finishing is of course to improve the mechandise qualities and the poor natures of silk so that it may be available as the best textile fiber in the world. Sometimes, famous trade marked textile plays more power than its quality in the silk market, nevertheless, this should be over line of research activities. Meantime, the silk demand has been also transferred from ladies stocking to other clothes since nylon or other synthetic fibers were developed. That is why, the extension of silk demand should be developed by various research works. Specially, silk is known as difficult textile to handle it during washing or ironing process which happened to depress down the silk usage for house wives. In order to solve such problems, the reporter has been worked for many years and now, he believes that he has developed a proper finishing method to coversuch problems. The developed finishing method may be said to eligible with economical aspect and shorten the dry duration after water washing in half against normal silk textile without harming the specific silk nature. As all of us know, silk fiber starts to denature since it was spinned by silkworm and the fiber is formed as overlapped "S" type curves during its concooning process. After it is made as raw silk or sericin silk, it shows as straight line form, but it changes in to waved form in case refining or degumming process in order return to its original spinned form. Such nature is continued during its textile form and ends with hard ironing nature than other textile fibers. Mean while, the silk fiber keeps to continue its denaturing and this is iniciated by repeat of washing and drying which takes many years to reach its final stage, The reporter has found the iniciating denature of silk by his finishing process, with out heat, decreasing the swollen nature which ended with shortening the drying duration after wash. Each washing was carried out by soaking the previously weighed sample in cold water for one hour, then pressed the sample for ten minutes to eliminate its free water component before weighing with same condition. According to this, the treated silk showed much denaturing after the finishing, but the standard silk progressed the denaturing by and by with the repeat of washing and drying, finally reached the same swollen degree of treated silk, Such treating result explains that the treated silk happened to be stebilized nature by the treating immediately. On the other that the treated silk happened to be stebilized nature by the treating immediately. On the other hand, standard silk may reach to such condition by the time of worn out clothes after repeat of washing and drying for many years while the clothes will be no more useful. The decreased swelling nature has brought about the drying period in half against standard silk after all. Not only the tests of tenacity and elongation but also crease resistance recovery, stiffness and shrinkage tests were carried out after each washing and drying which he has found better result on the treated silk textile against the standard silk. The most important thing was to keep the textile feeling of silk by such finishing work before improve any poor nature of silk. The general silk has a nature to absorb smoke or dirt from its surrounding air and reaches to dirty color shade upon such exposure, but the treated one has improved such nature because of its artificial denaturing, another word, it keeps clean longer than the normal silk. Many previous finishing works could improve some specific nature of silk, but it happened to deprave other important natures. The reporters work is, however, specialized to improve the silk to be useful as Wash and Wear Silk without harming its standard natures. So far, this work happened to be a overall innovative finishing method of silk textile.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.253-266
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• 2018

This paper proposes a methodology applying sequence tagging methodology to improve the performance of NER(Named Entity Recognition) used in QA system. In order to retrieve the correct answers stored in the database, it is necessary to switch the user's query into a language of the database such as SQL(Structured Query Language). Then, the computer can recognize the language of the user. This is the process of identifying the class or data name contained in the database. The method of retrieving the words contained in the query in the existing database and recognizing the object does not identify the homophone and the word phrases because it does not consider the context of the user's query. If there are multiple search results, all of them are returned as a result, so there can be many interpretations on the query and the time complexity for the calculation becomes large. To overcome these, this study aims to solve this problem by reflecting the contextual meaning of the query using Bidirectional LSTM-CRF. Also we tried to solve the disadvantages of the neural network model which can't identify the untrained words by using ontology knowledge based feature. Experiments were conducted on the ontology knowledge base of music domain and the performance was evaluated. In order to accurately evaluate the performance of the L-Bidirectional LSTM-CRF proposed in this study, we experimented with converting the words included in the learned query into untrained words in order to test whether the words were included in the database but correctly identified the untrained words. As a result, it was possible to recognize objects considering the context and can recognize the untrained words without re-training the L-Bidirectional LSTM-CRF mode, and it is confirmed that the performance of the object recognition as a whole is improved.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.145-165
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• 2023

Recommendation system plays a significant role on relieving difficulties of selecting information among rapidly increasing amount of information caused by the development of the Internet and on efficiently displaying information that fits individual personal interest. In particular, without the help of recommendation system, E-commerce and OTT companies cannot overcome the long-tail phenomenon, a phenomenon in which only popular products are consumed, as the number of products and contents are rapidly increasing. Therefore, the research on recommendation systems is being actively conducted to overcome the phenomenon and to provide information or contents that are aligned with users' individual interests, in order to induce customers to consume various products or contents. Usually, collaborative filtering which utilizes users' historical behavioral data shows better performance than contents-based filtering which utilizes users' preferred contents. However, collaborative filtering can suffer from cold-start problem which occurs when there is lack of users' historical behavioral data. In this paper, hybrid music recommendation system, which can solve cold-start problem, is proposed based on the playlist data of Melon music streaming service that is given by Kakao Arena for music playlist continuation competition. The goal of this research is to use music tracks, that are included in the playlists, and metadata of music tracks and playlists in order to predict other music tracks when the half or whole of the tracks are masked. Therefore, two different recommendation procedures were conducted depending on the two different situations. When music tracks are included in the playlist, LightFM is used in order to utilize the music track list of the playlists and metadata of each music tracks. Then, the result of Item2Vec model, which uses vector embeddings of music tracks, tags and titles for recommendation, is combined with the result of LightFM model to create final recommendation list. When there are no music tracks available in the playlists but only playlists' tags and titles are available, recommendation was made by finding similar playlists based on playlists vectors which was made by the aggregation of FastText pre-trained embedding vectors of tags and titles of each playlists. As a result, not only cold-start problem can be resolved, but also achieved better performance than ALS, BPR and Item2Vec by using the metadata of both music tracks and playlists. In addition, it was found that the LightFM model, which uses only artist information as an item feature, shows the best performance compared to other LightFM models which use other item features of music tracks.